We extract motorcycle specifications, city-level on-road pricing, dealer intelligence, and used bike inventory from Bikewale. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for New Bike Specs objects from bikewale.com. All fields typed and schema-versioned.
"brand": "Royal Enfield", "model": "Classic 350", "variant": "Dual Channel ABS", "engine_cc": 349.34, "max_power": "20.2 bhp @ 6100 rpm", "max_torque": "27 Nm @ 4000 rpm", "mileage_kmpl": 35, "transmission": "5 Speed Manual", "kerb_weight": 195
| # | brand | model | variant | engine_cc | max_power | max_torque |
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Complete list of extractable fields for On-Road Pricing objects from bikewale.com. All fields typed and schema-versioned.
"model": "Classic 350", "variant": "Dual Channel ABS", "city": "Bengaluru", "state": "Karnataka", "ex_showroom_price": 220990, "rto_charges": 45198, "insurance_cost": 12450, "on_road_price": 278638, "price_timestamp": "2026-05-12T10:00:00Z"
| # | model | variant | city | state | ex_showroom_price | rto_charges |
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Complete list of extractable fields for Used Bike Listings objects from bikewale.com. All fields typed and schema-versioned.
"listing_id": "BWU892144", "brand": "Honda", "model": "Activa 6G", "manufacture_year": 2021, "km_driven": 14500, "owner_number": 1, "location_city": "Mumbai", "asking_price": 55000, "seller_type": "Individual"
| # | listing_id | brand | model | manufacture_year | km_driven | owner_number |
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Complete list of extractable fields for Dealer Intelligence objects from bikewale.com. All fields typed and schema-versioned.
"dealer_name": "CVS Motors", "brand": "TVS", "city": "Bengaluru", "pincode": "560001", "contact_number": "+91-9876543210", "rating": 4.2, "review_count": 312, "latitude": 12.971598, "longitude": 77.594562
| # | dealer_name | brand | city | address | pincode | contact_number |
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Complete list of extractable fields for User Reviews objects from bikewale.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "model": "TVS Jupiter", "overall_rating": 4.5, "mileage_rating": 4.0, "comfort_rating": 5.0, "performance_rating": 4.0, "review_title": "Excellent family scooter", "date_posted": "2026-04-20"
| # | review_id | model | user_name | overall_rating | mileage_rating | comfort_rating |
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Our Bikewale scraper isolates granular specification matrices, normalises EV vs ICE metrics, and resolves dynamic city-level pricing via programmatic location injection.
Capture dimensions, engine displacement, torque, braking systems, and suspension details across thousands of active and discontinued models.
Programmatic cookie injection to simulate user location across tier-1, tier-2, and tier-3 cities, extracting precise RTO and insurance breakdowns.
Normalised schema for electric vehicles, capturing battery capacity, charging time, claimed range, and motor power ratings.
Extract asking prices, odometer readings, owner history, and location data from the used bike marketplace with infinite-scroll pagination handling.
Extract showroom names, addresses, geocoordinates, and contact numbers to map OEM footprints across India.
Extract structured user reviews, including granular ratings for comfort, performance, and mileage, alongside raw text for NLP pipelines.
Map available colour schemes to specific variants, capturing pricing premiums associated with specific paint options.
Extract default down-payment assumptions and interest rate calculations from embedded finance calculators.
Scrape Bikewale's proprietary alternative suggestions and comparison matrices to understand market positioning.
Brief in. Clean data out.
Provide specific OEMs, categories (e.g., EV scooters), or cities. We map the extraction schema to your requirements.
We configure Playwright spiders, handle location cookie injection for city prices, and bypass rate limits on bikewale.com.
Schema validation, null-rate checks on critical fields like ex-showroom price, and variant normalisation.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting national pricing matrices means spoofing location headers across hundreds of Indian cities simultaneously.
Bikewale relies on session cookies and local storage to display city-specific on-road prices. We programmatically inject location coordinates and city IDs during the Playwright session to extract accurate RTO and insurance data across multiple geographies.
The used bike section utilises infinite scroll and dynamic XHR loading. Our crawlers intercept the underlying API responses rather than relying on brittle DOM scraping, ensuring zero data loss across deep inventory pages.
Internal combustion engines and electric vehicles have distinct specification sets (displacement vs battery capacity). Our pipeline normalises these distinct DOM structures into a unified, queryable schema.
EMI estimates and insurance breakdowns are heavily JavaScript-dependent. We execute full browser sessions to trigger the hydration of these widgets before extraction.
Bikewale implements strict request limits per IP. We distribute crawls across a pool of Indian residential IPs, managing concurrency to stay below detection thresholds while maintaining high throughput.
OEMs track competitor on-road prices, RTO variations, and variant premiums across different states to optimise their own pricing strategies.
Analysts track EV scooter penetration, new model launches, and specification trends to identify shifts in consumer preference.
Auto-tech platforms train machine learning pricing models on historical used bike listings, correlating depreciation with age, mileage, and condition.
Brands map the geographic footprint of rival dealer networks to identify underserved markets and expansion opportunities.
Product teams mine granular user reviews to identify recurring reliability issues, comfort complaints, or performance praise for specific models.
Financial institutions correlate bike models with RTO charges and insurance quotes to refine their lending and premium calculation models.
"Bikewale holds the most granular two-wheeler pricing and specification dataset in India, but accessing city-level on-road breakdowns requires distributed state management."
Extracting national pricing matrices means spoofing location headers across hundreds of Indian cities simultaneously. DataFlirt handles the proxy rotation, session state, and JavaScript execution required to map every variant's RTO and insurance variations at scale, delivering analysis-ready data.
Everything supported by our bikewale.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy manages crawl orchestration and deduplication, while Playwright handles JavaScript rendering and location-based cookie injection required for pricing.
We utilise Indian residential proxy pools to ensure requests appear as legitimate domestic traffic, preventing geoblocking and rate limiting.
Pipelines run on AWS ECS with Airflow managing scheduling and dependency execution. All state is maintained in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About bikewale.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Bikewale is generally permissible under Indian law. DataFlirt targets only public, non-authenticated vehicle specifications, pricing, and dealer data. We do not extract personal user data or circumvent authentication walls.
Bikewale determines pricing based on session location. We programmatically inject city IDs and coordinates into the Playwright session cookies, allowing us to iterate through a predefined list of cities and extract the specific RTO and insurance breakdowns for each.
Yes. We can configure daily delta runs that identify newly added used bike listings and detect price drops on existing inventory, delivering only the changed records to minimise processing overhead.
Yes. Our schema dynamically adapts to the vehicle type. For EVs, it captures battery capacity, claimed range, charging time, and motor power, whereas ICE vehicles return displacement, mileage, and fuel capacity.
Dealer networks change infrequently. We typically recommend a monthly or quarterly refresh for showroom locations and contact details, though the cadence can be configured to your requirements.
Engagements typically start with a defined set of OEMs or a specific category (e.g., all EV scooters) across top tier-1 and tier-2 cities. Contact us with your specific data requirements for scoping.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of current EV specifications or a continuous feed of used vehicle inventory across India — we scope, build, and operate the pipeline.
